Hélène Massam
- Statistics and Probability top 1%
- Artificial Intelligence top 5%
- Applied Mathematics top 5%
- Computational Theory and Mathematics top 10%
- Mathematical Physics top 10%
- Co-authors
- Gérard LetacPiotr GraczykErhard NeherCarla M. CarvalhoMichael L. WestJacek WesołowskiD. A. S. FraserMathias Drton
- Topics
- Bayesian Methods and Mixture Models (18 papers)Statistical Methods and Bayesian Inference (14 papers)Bayesian Modeling and Causal Inference (13 papers)
- Journals
- Journal of the American Statistical AssociationBiometrikaJournal of the Royal Statistical Society Series B (Statistical Methodology)
- Partner nations
- CanadaFranceUnited States
In The Last Decade
Hélène Massam
50 papers receiving 655 citations
Peers
Comparison fields: 5 of 91
- Statistics and Probability 384
- Artificial Intelligence 267
- Applied Mathematics 94
- Computational Theory and Mathematics 72
- Mathematical Physics 62
Countries citing papers authored by Hélène Massam
This map shows the geographic impact of Hélène Massam's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Hélène Massam with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Hélène Massam more than expected).
Fields of papers citing papers by Hélène Massam
This network shows the impact of papers produced by Hélène Massam. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Hélène Massam. The network helps show where Hélène Massam may publish in the future.
Co-authorship network of co-authors of Hélène Massam
This figure shows the co-authorship network connecting the top 25 collaborators of Hélène Massam. A scholar is included among the top collaborators of Hélène Massam based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Hélène Massam. Hélène Massam is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 11 | |
| 2 | 4 | |
| 3 | 2 | |
| 4 | 1 | |
| 5 | The ratio of normalizing constants for Bayesian graphical Gaussian model selection | 2 |
| 6 | A Mixed Prinmal-Dual Bases Algorithm for Regression under Inequality Constraints. Application to Concave regression | 8 |
| 7 | 4 | |
| 8 | 1 | |
| 9 | 20 | |
| 10 | 5 | |
| 11 | 20 | |
| 12 | 1 | |
| 13 | 21 | |
| 14 | 13 | |
| 15 | 9 | |
| 16 | Les femmes font des maths | 2 |
| 17 | 2 | |
| 18 | 1 | |
| 19 | 2 | |
| 20 | 13 |
About Hélène Massam
Hélène Massam is a scholar working on Statistics and Probability, Applied Mathematics and Artificial Intelligence, having authored 50 papers that have together received 696 indexed citations. Recurring topics across this work include Bayesian Methods and Mixture Models (18 papers), Statistical Methods and Bayesian Inference (14 papers) and Bayesian Modeling and Causal Inference (13 papers). The work is most often cited by research in Statistics and Probability (384 citations), Discrete Mathematics and Combinatorics (51 citations) and Applied Mathematics (94 citations). Hélène Massam has collaborated with scholars based in Canada, France and United States. Frequent co-authors include Gérard Letac, Piotr Graczyk, Erhard Neher, Carla M. Carvalho, Michael L. West, Gérard Letac, Jacek Wesołowski, D. A. S. Fraser, Mathias Drton and Ingram Olkin. Their work appears in journals such as Journal of the American Statistical Association, Biometrika and Journal of the Royal Statistical Society Series B (Statistical Methodology).
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.